Multilingual intelligent assistants, such as ChatGPT, have recently gained popularity. To further expand the applications of multilingual artificial intelligence assistants and facilitate international communication, it is essential to enhance the performance of multilingual speech recognition, which is a crucial component of speech interaction. In this paper, we propose two simple and parameter-efficient methods: language prompt tuning and frame-level language adapter, to respectively enhance language-configurable and language-agnostic multilingual speech recognition. Additionally, we explore the feasibility of integrating these two approaches using parameter-efficient fine-tuning methods. Our experiments demonstrate significant performance improvements across seven languages using our proposed methods.

翻译：近年来，多语言智能助手（如ChatGPT）逐渐普及。为进一步拓展多语言人工智能助手的应用场景并促进国际交流，提升作为语音交互核心组件的多语言语音识别性能显得尤为重要。本文提出两种参数高效且简洁的方法：语言提示调优（Language Prompt Tuning）与帧级语言适配器（Frame-Level Language Adapter），分别增强语言可配置型与语言无关型多语言语音识别。此外，我们探索了通过参数高效微调方法融合这两种技术路线的可行性。实验表明，本文方法在七种语言上均取得了显著的性能提升。